Sr SQL/Oracle DBA
Listed on 2026-09-04
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Software Development
AI Engineer (Applied/Software), Data Engineering, Machine Learning/ ML Engineer
If you are a passionate database engineer who would love to sit at the intersection of traditional enterprise database engineering and the emerging Gen AI revolution — building next-generation, AI-powered data solutions that transform how tens of thousands of users interact with the largest Human Capital Management system then we have a perfect role for you and we would like to have a discussion with you.
Education/ Experience / Certification
Bachelor's degree in computer science or related field (required)
Master's degree in computer science, Data Science, or AI/ML (preferred)
10+ years of experience as a Database Developer/DBA in a fast-paced agile environment
7+ years as an Oracle Developer/DBA with expert-level PL/SQL and SQL skills
2+ years of hands-on experience with Generative AI, LLMs, or ML pipelines in a production environment (preferred)
Deep expertise in database internals, expert-level PL/SQL, SQL, and Python/Shell programming
AWS Certified Machine Learning Specialty (plus)
Core Database EngineeringDesign and build scalable database services and solutions to complex business problems
Debug critical database issues and provide root-cause analysis with long-term solutions
Research, Design, Develop, and/or modify applications using SQL, PL/SQL, Python, and AI-assisted development tools
Prototype solutions and recommend adoption of new technologies including Generative AI and LLM-powered database tooling
Development and Deployment of application database changes/releases across production and non-production environments
Build and maintain LLM-powered database assistants using frameworks such as Lang Chain, Llama Index, or similar
Develop Retrieval-Augmented Generation (RAG) solutions using structured/unstructured data from Oracle and PostgreSQL databases
Integrate AI-powered query optimization tools to enhance database performance tuning workflows
Leverage Git Hub Copilot, Amazon Q, or similar AI coding assistants to accelerate PL/SQL and Python development
Build vector database integrations (pgvector, Oracle AI Vector Search) for semantic search capabilities
Evaluate and adopt AI/ML model serving patterns (batch vs. real-time inference) for database-adjacent workloads
Drive prompt engineering best practices for database-related AI applications
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